




"""simple docstring"""






from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

lowercase__			=		datasets.load_iris()

lowercase__			=		np.array(data["""data"""])
lowercase__			=		np.array(data["""target"""])
lowercase__			=		data["""target_names"""]

lowercase__		,				lowercase__		,				lowercase__		,				lowercase__			=		train_test_split(X, y)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				return np.linalg.norm(np.array(UpperCamelCase__						) - np.array(UpperCamelCase__						)						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__=5						):
				_lowerCamelCase	:     List[Any]       =       zip(UpperCamelCase__				,       UpperCamelCase__						)
				# List of distances of all points from the point to be classified
				_lowerCamelCase	:     str       =       []
				for data_point in data:
								_lowerCamelCase	:     Optional[int]       =       euclidean_distance(data_point[0]				,       UpperCamelCase__						)
								distances.append((distance, data_point[1])						)
				# Choosing 'k' points with the least distances.
				_lowerCamelCase	:     Optional[int]       =       [i[1] for i in sorted(UpperCamelCase__						)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				_lowerCamelCase	:     List[Any]       =       Counter(UpperCamelCase__						).most_common(1						)[0][0]
				return classes[result]


if __name__ == "__main__":
			print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
'''simple docstring'''




def __lowerCAmelCase					(				)      ->       Optional[Any]:
    __lowerCamelCase            =    0
    for i in range(1							,    10_01							):
        total += i**i
    return str(UpperCamelCase__							)[-10:]


if __name__ == "__main__":
  print(solution())
